595 citations · 3.6k across the 131 of their papers we have counts for
21 papers · 1 filter
Deep Unfolding: Recent Developments, Theory, and Design Guidelines
Nir Shlezinger, Santiago Segarra, Yi Zhang +4
Optimization methods play a central role in signal processing, serving as the mathematical foundation for inference, estimation, and control. While classical iterative optimization…
Online Learning of Modular Bayesian Deep Receivers: Single-Step Adaptation with Streaming Data
Yakov Gusakov, Osvaldo Simeone, Tirza Routtenberg +1
Deep neural network (DNN)-based receivers offer a powerful alternative to classical model-based designs for wireless communication, especially in complex and nonlinear propagation…
AI-Aided Annealed Langevin Dynamics for Rapid Optimization of Programmable Channels
Tomer Shaked, Philipp del Hougne, George C. Alexandropoulos +1
Emerging technologies such as Reconfigurable Intelligent Surfaces (RIS) make it possible to optimize some parameters of wireless channels. Conventional approaches require relating…
Leaky Wave Antennas for Next Generation Wireless Applications in sub-THz Frequencies: Current Status and Research Challenges
Natalie Lang, Atsutse K. Kludze, Nir Shlezinger +4
The ever-growing demand for ultra-high data rates, massive connectivity, and joint communication-sensing capabilities in future wireless networks is driving research into sub-terah…
DiffAU: Diffusion-Based Ambisonics Upscaling
Amit Milstein, Nir Shlezinger, Boaz Rafaely
Spatial audio enhances immersion by reproducing 3D sound fields, with Ambisonics offering a scalable format for this purpose. While first-order Ambisonics (FOA) notably facilitates…
Attention-Enhanced Learning for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications
Mengyuan Ma, Nhan Thanh Nguyen, Nir Shlezinger +2
Beam training and prediction in millimeter-wave communications are highly challenging due to fast time-varying channels and sensitivity to blockages and mobility. In this context,…
Knowledge Distillation for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications
Mengyuan Ma, Nhan Thanh Nguyen, Nir Shlezinger +3
Infrastructure-mounted sensors can capture rich environmental information to enhance communications and facilitate beamforming in millimeter-wave systems. This work presents an eff…
Deep Unfolding with Approximated Computations for Rapid Optimization
Dvir Avrahami, Amit Milstein, Caroline Chaux +2
Optimization-based solvers play a central role in a wide range of signal processing and communication tasks. However, their applicability in latency-sensitive systems is limited by…
Extremely Large-Scale Dynamic Metasurface Antennas for 6G Near-Field Networks: Opportunities and Challenges
Haiyang Zhang, Nir Shlezinger, Giulia Torcolacci +6
6G networks will need to support higher data rates, high-precision localization, and imaging capabilities. Near-field technologies, enabled by extremely large-scale (XL)-arrays, ar…
Hybrid RISs for Simultaneous Tunable Reflections and Sensing
George C. Alexandropoulos, Nir Shlezinger, Ioannis Gavras +1
The concept of smart wireless environments envisions dynamic programmable propagation of information-bearing signals through the deployment of Reconfigurable Intelligent Surfaces (…
OLALa: Online Learned Adaptive Lattice Codes for Heterogeneous Federated Learning
Natalie Lang, Maya Simhi, Nir Shlezinger
Federated learning (FL) enables collaborative training across distributed clients without sharing raw data, often at the cost of substantial communication overhead induced by trans…
In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory
Matteo Zecchin, Tomer Raviv, Dileep Kalathil +3
In recent years, deep learning has facilitated the creation of wireless receivers capable of functioning effectively in conditions that challenge traditional model-based designs. L…
EOTNet: Deep Memory Aided Bayesian Filter for Extended Object Tracking
Zhixing Wang, Le Zheng, Shi Yan +3
Extended object tracking methods based on random matrices, founded on Bayesian filters, have been able to achieve efficient recursive processes while jointly estimating the kinemat…
Memory-Efficient Distributed Unlearning
Natalie Lang, Alon Helvitz, Nir Shlezinger
Machine unlearning considers the removal of the contribution of a set of data points from a trained model. In a distributed setting, where a server orchestrates training using data…
Adaptive Deadline and Batch Layered Synchronized Federated Learning
Asaf Goren, Natalie Lang, Nir Shlezinger +1
Federated learning (FL) enables collaborative model training across distributed edge devices while preserving data privacy, and typically operates in a round-based synchronous mann…
Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers
Elad Sofer, Tomer Shaked, Caroline Chaux +1
Machine learning (ML) models are often sensitive to carefully crafted yet seemingly unnoticeable perturbations. Such adversarial examples are considered to be a property of ML mode…
Near Field Localization via AI-Aided Subspace Methods
Arad Gast, Luc Le Magoarou, Nir Shlezinger
The increasing demands for high-throughput and energy-efficient wireless communications are driving the adoption of extremely large antennas operating at high-frequency bands. In t…
Optimal ISAC Beamforming Structure and Efficient Algorithms for Sum Rate and CRLB Balancing
Tianyu Fang, Mengyuan Ma, Markku Juntti +3
Integrated sensing and communications (ISAC) has emerged as a promising paradigm to unify wireless communications and radar sensing, enabling efficient spectrum and hardware utiliz…
PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learning
Ori Peleg, Natalie Lang, Dan Ben Ami +3
Federated learning (FL) enables multiple edge devices to collaboratively train a machine learning model without the need to share potentially private data. Federated learning proce…
Deep Variational Sequential Monte Carlo for High-Dimensional Observations
Wessel L. van Nierop, Nir Shlezinger, Ruud J. G. van Sloun
Sequential Monte Carlo (SMC), or particle filtering, is widely used in nonlinear state-space systems, but its performance often suffers from poorly approximated proposal and state-…
Remote Inference over Dynamic Links via Adaptive Rate Deep Task-Oriented Vector Quantization
Eyal Fishel, May Malka, Shai Ginzach +1
A broad range of technologies rely on remote inference, wherein data acquired is conveyed over a communication channel for inference in a remote server. Communication between the p…